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A Social Outcomes and Priorities centered (SOP) Framework for AI policy

arXiv 2411.08241 ↗First posted 12 Nov 2024cs.CY cs.AI cs.LGcited by 0

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Abstract (arXiv)

Rapid developments in AI and its adoption across various domains have necessitated a need to build robust guardrails and risk containment plans while ensuring equitable benefits for the betterment of society. The current technology-centered approach has resulted in a fragmented, reactive, and ineffective policy apparatus. This paper highlights the immediate and urgent need to pivot to a society-centered approach to develop comprehensive, coherent, forward-looking AI policy. To this end, we present a Social Outcomes and Priorities centered (SOP) framework for AI policy along with proposals on implementation of its various components. While the SOP framework is presented from a US-centric view, the takeaways are general and applicable globally.

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13 of 84 references.

References · 13

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  3. 2020Extracting Training Data from Large Language Modelscited by 2,144
  4. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  5. 2022Quantifying Memorization Across Neural Language Modelscited by 755
  6. 2023Scalable Extraction of Training Data from (Production) Language Modelscited by 415
  7. 2022What Does it Mean for a Language Model to Preserve Privacy?cited by 194
  8. 2024Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Toolscited by 170
  9. 2024SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoningcited by 90
  10. 2024Here Comes The AI Worm: Unleashing Zero-click Worms that Target GenAI-Powered Applicationscited by 58
  11. 2024What's in a Name? Auditing Large Language Models for Race and Gender Biascited by 50
  12. 2023Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectivescited by 31
1 more
  1. 2024On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigationcited by 8

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